Hi All,

 

I am going to compare a few spatial interpolation techniques including
kriging with an external drift (KED) and ordinary co-kriging (OCK) (such as
those defined in: Goovaerts, 1997. Geostatistics for Natural Resources
Evaluation.) to interpolate marine sediment data (mud content in this case)
using bathymetry as a secondary variable. However, it seems that the ordinary
cokriging in gstat as shown in demo(cokriging) is different from the OCK we
planned to use. Is it possible to do such OCK in gstat? Any comments and
example? Thanks.

 

As to KED, I tried 

> vgm1 <- variogram(sqrt(mud)~bathy, data.file.dev)

> model.1 <- fit.variogram(vgm1,vgm(1,"Sph",5,1))

> # plot(vgm1, model.1)

> coordinates(data.file.pred) = ~LON+LAT

> mud.ok <- krige(sqrt(mud)~bathy, data.file.dev, data.file.pred, model =
model.1)

[using universal kriging]

 

> vgm1 <- variogram(sqrt(mud)~LON+LAT, data.file.dev)

> model.1 <- fit.variogram(vgm1,vgm(1,"Sph",5,1))

> # plot(vgm1, model.1)

> coordinates(data.file.pred) = ~LON+LAT

> mud.ok <- krige(sqrt(mud)~LON+LAT, data.file.dev, data.file.pred, model =
model.1)

[using universal kriging]

 

Both of them are UK. But the first one seems regression kriging. Is it
identical to KED in this case? If not, any comments and examples of KED are
appreciated.

 

Cheers,

 

Jin

--------------------------------------------

Jin Li, PhD

Spatial Modeller/

Computational Statistician

Marine & Coastal Environment

Geoscience Australia



Ph: 61 (02) 6249 9899

Fax: 61 (02) 6249 9956

email: jin.li@ga.gov.au <mailto:jin.li@ga.gov.au> 

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